智能控制技术是废水处理过程中减少温室气体（GHG）排放和能源消耗的切实手段，而且，机器学习（ML）和多模态技术为智能控制技术提供了一种更为前景广阔的解决方案。在本研究中提出了一种基于多模态策略的机器学习技术，并将其应用于验证污水处理厂（WWTP）的智能曝气控制。研究结果表明：基于八种传统的机器学习算法耦合视觉模型构建的多模态融合模型在污水处理厂智能曝气控制的可行性，使用多模态融合模型大大提高了传统机器学习模型的性能和曝气控制效率，表现出卓越的性能和可解释性。在多模态模型中，将随机森林与视觉模型相结合可实现最高的曝气量预测精度，平均绝对百分比误差为 4.4%，决定系数为 0.948。实际污水处理厂的应用研究表明：与传统的模糊控制方法相比，多模态融合模型方法可降低 19.8% 的运行成本。与此同时，本研究还讨论了多模态策略在水务领域的潜在应用，同时为了提高模型的公平性并促进广泛应用，本研究的多模态融合模型可在 GitHub 上免费获取，以旨在消除人工智能技术在污水处理领域的应用障碍，推动人工智能在城市污水处理中的应用。

The potential for reducing greenhouse gas (GHG) emissions and energy consumption in wastewater treatment can be realized through intelligent control, with machine learning (ML) and multimodality emerging as a promising solution. Here, we introduce an ML technique based on multimodal strategies, focusing specifically on intelligent aeration control in wastewater treatment plants (WWTPs). The generalization of the multimodal strategy is demonstrated on eight ML models. The results demonstrate that this multimodal strategy significantly enhances model indicators for ML in environmental science and the efficiency of aeration control, exhibiting exceptional performance and interpretability. Integrating random forest with visual models achieves the highest accuracy in forecasting aeration quantity in multimodal models, with a mean absolute percentage error of 4.4% and a coefficient of determination of 0.948. Practical testing in a full-scale plant reveals that the multimodal model can reduce operation costs by 19.8% compared to traditional fuzzy control methods. The potential application of these strategies in critical water science domains is discussed. To foster accessibility and promote widespread adoption, the multimodal ML models are freely available on GitHub, thereby eliminating technical barriers and encouraging the application of artificial intelligence in urban wastewater treatment.